The invention relates generally to the field of digital image processing, and in particular, to the identification of and the reduction of the red-eye effect in images.
The increased use of computers in many applications has drawn increasing focus on improving the man-machine interface. It is the desire of many applications to locate the face of the user in an image, then to process it to robustly identify the person. The algorithms for facial recognition have dramatically improved in recent years and are now sufficiently robust for many applications. The weak part of the system is the face detection and location. Other applications for facial imaging beyond identification are also growing in interest, in particular perceptual computing, such as discerning a reaction or emotion from a user's face. This would enable computer-driven systems to be more responsive, like a human. Again, these algorithms will be limited by the weaknesses in face detection and location.
When flash illumination is used during the capture of an image that contains sizable human faces, the pupils of people sometimes appear red because the light is partially absorbed by capillaries in the retina. As illustrated in FIG. 1, the light rays 10 from the flash illumination source 12 enter the eye 14 through the eye lens 16, and form an image 18 of the illumination source 12 on retina 17. The eye-defect in captured images, known as the “red-eye effect” is mostly seen with human eyes. In case animals are captured, the eye-defect will show a bright green or yellow color. Animal eyes are generally more difficult to detect for pattern recognition algorithms due to the large variations in animal facial structure, complexion, hair and structure of the eyes itself.
Referring to FIG. 2, the light rays 30 reflected from the retina 17 exit the eye 14 through the eye lens 16, and finally enter the camera lens 32. If the camera lens 32 is placed close to the illumination source 12, the red-eye effect will be maximized. In other words, the amount of red-eye or eye-defect being observed increases as the illumination source 12 gets closer to an optical axis 34 defined by the camera lens 32.
The general technique for red-eye reduction within cameras has been to impact two parameters: (a) reduce the pupil diameter of the subject, for example by emitting a series of small pre-flashes prior to capturing the desired image with full illumination; and, (b) increase the flash to lens separation, so that the illumination impinging on the subjects eyes is reflected at an angle that misses the taking lens.
In most cases, where a flash is needed to illuminate the subject, the subject's pupils are dilated due to the low ambient illumination. Light from the flash can then enter the eye through the pupil and is reflected off the blood vessels at the back of the retina. This reflection may be recorded by the camera if the geometry of the camera lens, the flash, and the subject's eyes is just right, rendering the captured image unpleasant and objectionable to viewers. Hence there is a significant need for automatic methods that identify and correct red-eye areas in a captured image.
A number of methods have been proposed for detecting and/or removing red-eye artifacts that result in the images themselves. The majority of these methods are either (i) supervised; i.e. they require the user to manually identify the subregions in an image where the artifacts are observed, or (ii) dependent on skin/face and/or eye detection to find the areas of interest. However, manual user identification is cumbersome for the user, especially when a lot of images are involved. In addition, typical skin, face, and eye detection techniques are computationally intensive.